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Sparse representation scheme with enhanced medium pixel intensity for face recognition

CAAI Transactions on Intelligence technology 2024年第1期9卷 116-127页

作者： Xuexue Zhang Yongjun Zhang Zewei Wang Wei Long Weihao Gao Bob Zhang State Key Laboratory of Public Big Data Institute for Artificial IntelligenceCollege of Computer Science and TechnologyGuizhou UniversityGuiyangGuizhouChina Department of Computer and Information Science University of MacaoAvenida da UniversidadeTaipaMacaoChina

Sparse representation is an effective data classification algorithm that depends on the known training samples to categorise the test *** has been widely used in various image classification *** in sparse representation means that only a few of instances selected from all training samples can effectively convey the essential class-specific information of the test sample,which is very important for *** deformable images such as human faces,pixels at the same location of different images of the same subject usually have different ***,extracting features and correctly classifying such deformable objects is very ***,the lighting,attitude and occlusion cause more *** the problems and challenges listed above,a novel image representation and classification algorithm is ***,the authors’algorithm generates virtual samples by a non-linear variation *** method can effectively extract the low-frequency information of space-domain features of the original image,which is very useful for representing deformable *** combination of the original and virtual samples is more beneficial to improve the clas-sification performance and robustness of the ***,the authors’algorithm calculates the expression coefficients of the original and virtual samples separately using the sparse representation principle and obtains the final score by a designed efficient score fusion *** weighting coefficients in the score fusion scheme are set entirely ***,the algorithm classifies the samples based on the final *** experimental results show that our method performs better classification than conventional sparse representation algorithms.

关键词： computer vision face recognition image classification image representation

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Telugu Meme dataset and Baseline System for Automatic Identification of Domain, and Troll in Memes 2nd

Telugu Meme Dataset and Baseline System for Automatic Iden...

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2nd International Conference on Speech and Language Technologies for Low-Resource Languages, SPELLL 2023

作者： Lohith, N. Adnan Raqeeb, S. Sai Manoj Reddy, Poreddy Sunil Kumar, Chekuri Venkata Anand Kumar, M. Chakravarthi, Bharathi Raja Department of Information Technology National Institute of Technology Karnataka Surathkal India Insight SFI Research Centre for Data Analytics Data Science Institute National University of Ireland Galway Galway Ireland

ISBN: (纸本)9783031584947

Everyone now uses social media to communicate information and ideas, which has become a requirement for everyone. Social media posts can offend people or be helpful or educational for them. Memes are one type of media that is disseminated in this way through direct messages, videos, or photographs. A meme is an image or video that captures the opinions and sentiments of a particular group of people. Memes can be trolling or not, and they include an image and text that express an emotion. Most memes are unique to a particular linguistic group of people. The most frequent cause of this is different languages. Memes must be categorized since studying them can help us understand the interests of each user. As both are necessary for interpretation, the meme cannot be considered acceptable if we only gaze at the text or the image. In this article, we provided a method for categorizing memes in Telugu that considers both the meme’s text and visual features, classifies trolling and non-trolling memes, attempt to determine the emotion the meme conveys and categorizes the meme according to the domain it belongs to. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Social networking (online)

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Object Detection with YOLO Version 3 for Big data 1

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5th International Conference on information Systems and Management science, ISMS 2022

作者： Saxena, Rajat Dubey, Shatendra Karna, Vishma Kumar Big Data Lab Department of Computer Science and Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeth Vishwavidyalaya Indore India Big Data Lab Department of Information Technology NRI Institute of Information Science and Technology Bhopal India

ISBN: (数字)9783031311536

ISBN: (纸本)9783031311529

Object detection is the most vital task for the application of computer vision. The You only Look Once (YOLO) version3 is the most promising technique used for deep learning-based object detection. It is the k-means cluster method, which estimates initial weight and height of the predicting bounding boxes that are sensitive to the initial cluster centers. This paper focuses on study of approaches for efficient object detection techniques in the field of computer vision. The improvement in the accuracy had been studied based on the deep learning techniques. It aims to develop an online tool for a better study about the object detection techniques as a platform to review the object detection in both online and offline images. A popular state of art object detection algorithm called YOLOv3 which is trained on COCO and ImageNet. The result obtained in the developed online tool has shown a positive result with a possibility in detecting the most common objects available in this real world. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Smart Resume Analyser 15

Smart Resume Analyser

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Rane, Milind Zanwar, Harsh Wagh, Kunal Wakode, Suhani Yadav, Avishkar Kulkarni, Atul Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Pune India Department of Mechanical Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798331300579

Every time job recruiters (HR) have to go through a huge pile of resumes submitted by applicants. which becomes a very burdensome task, with Python, NumPy, and Pandas, a resume parser is to be created as part of the project. A structured format will be used to store the data that the parser extracts from resumes, including name, contact information, education, work experience, and abilities. Pandas will be used to store the data in a data frame, and NumPy will be used to manipulate the data. The parser will recognise and extract pertinent information from resumes using methods of natural language processing, improving the accuracy and efficiency of the procedure. By swiftly extracting pertinent information from resumes, the initiative will assist in automating the hiring process and save time for recruiters and HR personnel. © Grenze Scientific Society, 2024.

关键词： Python

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An Anomaly Detection Algorithm for Estimating Interannual Variation in Crop Yields 5

An Anomaly Detection Algorithm for Estimating Interannual Va...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Vinoth Kumar, R. Malathy Manjula, M. Veerandeswari, J. Indumathy, M. Raghul, B.S. Rajiv Gandhi College of Engineering & Technology Information Technology Department Puduherry Kriumambakkam India Data Science and Business Systems Srm Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798350383522

Crop production is a vital aspect of human survival. Reducing global poverty heavily relies on increasing the robustness in crop yields. The quantity of crops harvested from a given agricultural land, also known as crop yields of that land is the important parameter that helps to increase the smartness in adopting novel agricultural plans. Measuring this parameter can be done through supervised machine learning techniques which helps with accurate predictions given the historical data of crop yield. This paper proposes an ideation, that uses Support Vector Machines (SVMs) because of its strength being accuracy, robust and flexible. SVM can predict reliable crop yield estimates that help to allocate resources and smart planning for agricultural activities. Also, SVMs are good enough to handle the imperfections in historical crop yield datasets, outliers, and inconsistencies. Furthermore, SVMs can bring out the relationships and any nonlinear patterns in the crop yields which are influenced by uncertain environmental factors over various seasons. This helps to identify the anomalies in the historical data. The anomalies could be exceptionally high yield or the failures which happen to be pinpointed as abnormal years. The identified outliers are excluded by the SVM, thus bringing out a prediction model that focusses on 'normal years'. By this, it is possible to represent more accurate inter-annual trends of crop yields avoiding the anomalies aiding the farmer to make better decisions for much more productivity in the crop yield. Based on the ideation, simulations are performed that showcase the success of the SVM in predicting good for the crop yields in inter-annual period. © 2024 IEEE.

关键词： Anomaly detection crop yield inter-annual variability SVM

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Aquify:AI-Enhanced Predictive Analytics Toolkit For Water Contamination 5

Aquify:AI-Enhanced Predictive Analytics Toolkit For Water Co...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Sanath, G. Deekshitha, S. Shravya, H. Jayanthi, M.G. Kannadaguli, Prashanth Cambridge Institute of Technology Computer Science and Engineering Bengaluru India Cambridge Institute of Technology Information Science and Engineering Bengaluru India Senior Data Science Trainer Dhaarini Academy Bengaluru India

ISBN: (纸本)9798331528539

Effective monitoring of water quality remains a problem, in real-time contamination detection and prediction. Despite advancements in the sector, many existing approaches continue to rely on labor-intensive laboratory testing, which limits their capacity to give rapid and accurate results. By leveraging advanced ML/DL models, it is possible to build a more robust and adaptable platform for real-time analysis, prediction, and decision support. Approaches based on models such as Random Forest, XGBoost, CNN, RNN, LSTM, and GRU have been good with the accuracy of water quality predictions. They help identify pollution sources and diagnose water quality issues, providing essential information to stakeholders in sustainable water resource management. The ML and DL approaches help improve water quality monitoring and boost future research in environmental sustainability and natural resource management. © 2024 IEEE.

关键词： Resource allocation

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Leveraging machine learning for intelligent agriculture

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

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Enhancing Recruitment Processes Through Integration of Personality Traits and Professional Skills Analysis 5

Enhancing Recruitment Processes Through Integration of Perso...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： De Silva, Maleesha Zoysa, Sandani Maldeniya, Manushika De Silva, Shanali Karunasena, Anuradha Abeywardhan, Lakmini Sri Lanka Institute of Information Technology Department of Information Technology Specializes in Data Science Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

The success and growth of an organization are heavily dependent on the selection of the right candidates. The conventional approach of relying solely on resumes and academic backgrounds falls short in adequately evaluating a candidate's suitability for a job role. This research paper proposes a novel method of candidate recruitment that streamlines the process by integrating advanced machine learning algorithms and natural language processing techniques. This study introduces a new approach to measuring programming language proficiency by analyzing GitHub data and comparing candidates using a knowledge graph. LinkedIn skill data is used to predict job categories through a SVM multiclass classification machine learning model. Additionally, Referee feedback is evaluated using sentiment analysis to improve recommendations. The study also utilizes K-means clustering and NLP techniques to identify the Big Five personality traits distribution among candidates and assess their alignment with specific job roles. The XGBoost model shows the best performance in predicting the personality cluster of candidates. The findings of this study contribute to the growing field of HR technology and provide valuable insights for optimizing recruitment practices in the IT industry using machine learning. © 2023 IEEE.

关键词： Sentiment analysis

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Self- and Cross-Attention Enhanced Transformer for Visible and Thermal Infrared Hyperspectral Image Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 13408-13422页

作者： Zhao, Enyu Su, Yongfang Qu, Nianxin Wang, Yulei Gao, Caixia Zeng, Jian Information Science and Technology College Dalian11-6026 China Chinese Academy of Sciences Key Laboratory of Quantitative Remote Sensing Information Technology Aerospace Information Research Institute Beijing100094 China China Centre for Resources Satellite Data and Application Beijing100094 China

Visible hyperspectral image (V-HSI) and thermal infrared hyperspectral image (TI-HSI) have been crucial data sources for land cover classification. V-HSI can directly provide information of land surface, such as shape, color, texture, and other features. TI-HSI contains rich long-wave spectral information, which can reflect the unique emission characteristics of ground objects in the thermal infrared spectral range. To fully leverage the advantages of V-HSI and TI-HSI while enhancing the classification accuracy, this article proposes a self- and cross-attention enhanced transformer network (SCAET), integrated with convolutional neural network (CNN) for HSI classification. Initially, the proposed method employs a dual-branch spatial-spectral CNN (SS CNN) to extract spectral convolution features from V-HSI and TI-HSI, respectively. Subsequently, a spectral feature mapping (SFM) module is proposed to perform feature transformation, extracting independent and interactive features of V-HSI and TI-HSI. Then, a self- and cross-attention interactive enhancement module is designed to extract deeper features and enhance the independent features by using the interactive features. In addition, a self-projection mixing module is formulated to promote feature interaction and improve the generalization capability of the model. To validate the effectiveness of the proposed network, extensive experiments are conducted on real-world datasets, and the results indicate that SCAET significantly outperforms current multisource fusion networks. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Comparative Analysis of Regression Techniques for Accurate House Price Prediction Using Machine Learning: A Statistical Analysis Method 2

Comparative Analysis of Regression Techniques for Accurate H...

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2nd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2023

作者： Patel, Tanu Sahu, Jyoti Rao K, Malikharjuna International Institute of Information Technology Data Science and Artificial Intelligence Naya Raipur India

ISBN: (纸本)9798350333763

Accurate house price prediction is crucial in real estate. This research aims to build a reliable house price prediction model using regression techniques. We evaluate model performance through 3-fold cross-validation and employ the mean R2 score as the evaluation metric, measuring predictive capacity. The top-performing model undergoes hyperparameter tuning. Our findings shed light on the effectiveness of regression techniques in house price prediction, identifying the most accurate model. © 2023 IEEE.

关键词： Houses

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